An organization in Fort Worth, Texas is hiring an AI Engineer to design, build, and run production-grade generative AI applications. The top three must-haves are Generative AI production development, Python coding, and AI engineering. You will work with large language models, agentic orchestration, RAG architectures, and vector databases, with full software development lifecycle experience in large-scale, hosted production environments. Experience in the airline or transport industry is a plus.
About the Role — AI Engineer Fort Worth Texas
Focus: Building and operating generative AI solutions in production — not just prototypes
Core Technologies: Python, LLMs, agentic frameworks, prompt engineering, RAG, vector databases
Engineering Standard: Full SDLC, CI/CD, observability, monitoring, cost optimization, and drift detection
Governance: Responsible AI frameworks and controls are part of the job
Industry Plus: Airline or transport industry background is a bonus
AI Career Opportunity in Dallas–Fort Worth
Production Focus: Own generative AI systems from design through deployment and long-term maintenance
Modern Stack: Semantic Kernel, LangChain/LangGraph, Copilot Studio, and tool-calling patterns from OpenAI and Anthropic
AI-Assisted Development: Familiarity with tools like GitHub Copilot, Cursor, and Claude Code is a plus
DFW Tech Market: Fort Worth sits in one of the largest and fastest-growing technology job markets in the United States
Position Overview
The AI Engineer in Fort Worth designs, develops, and implements large-scale applications in hosted production environments, with a strong emphasis on generative AI. You will build agentic systems and LLM-based solutions, apply systematic prompt design, implement RAG patterns on vector databases, evaluate model output with methods such as LLM-as-a-judge and automated regression testing, and keep deployed models healthy through monitoring, cost control, and drift detection. You will also help put governance frameworks and controls in place for responsible AI, working in an Agile team on cloud platforms with modern CI/CD pipelines.
Why This Role Matters: Plenty of teams can demo a chatbot. Far fewer can keep a generative AI system accurate, affordable, and safe in production. This AI Engineer role is about exactly that — building agentic and LLM solutions that hold up under real workloads, and putting the evaluation, monitoring, and governance in place to keep them trustworthy.
Key Responsibilities
Generative AI & Agentic System Development
- Design, develop, and implement large-scale generative AI applications in hosted production environments
- Build agentic orchestration using frameworks such as Semantic Kernel, LangChain/LangGraph, Microsoft Copilot Studio, or OpenAI and Anthropic tool-calling patterns
- Apply systematic prompt design, few-shot learning, chain-of-thought, and prompt optimization for foundation models
- Implement RAG architecture patterns using vector databases such as Pinecone, FAISS, Weaviate, or Chroma
Evaluation, Monitoring & Responsible AI
- Evaluate model output using LLM-as-a-judge, automated benchmarking, and regression testing
- Maintain deployed models through observability, monitoring, cost optimization, and drift detection
- Develop and implement governance frameworks and controls for responsible AI
- Use experimentation and measurement practices — A/B testing, KPI instrumentation, and operational analytics
Software Engineering & Delivery
- Apply object-oriented design principles and write clean, maintainable Python (with working knowledge of Java or another enterprise language)
- Design and integrate RESTful APIs and microservices using JSON
- Work with CI/CD pipelines and DevOps tooling such as Git, GitHub, Jenkins, or Azure DevOps
- Build on cloud platforms (Azure, AWS, or GCP) using cloud-native patterns, working in Agile/SCRUM teams
Qualifications & Requirements
Minimum Qualifications
- Bachelor’s degree in Computer Science, Computer Engineering, Technology, Information Systems (CIS/MIS), Engineering, or a related technical discipline — or equivalent experience and training
- Full SDLC experience designing, developing, and implementing large-scale applications in hosted production environments
- Professional, design, and open-source experience
Preferred Qualifications
- Master’s degree in a related technical discipline, or equivalent experience
- Prior experience designing and building agentic systems
- Prior experience developing and implementing AI solutions with large language models
- Practical understanding of transformer architectures and how modern LLMs work
- Airline or transport industry experience is a plus
- Nice to have: broader coding engineering and data science skills
About the Fort Worth AI Opportunity
Fort Worth is part of the Dallas–Fort Worth metroplex, one of the biggest technology and corporate hubs in the country, with a strong presence from aviation, transportation, and logistics employers. For an AI engineer, that means real operational problems to solve at scale — from scheduling and customer service to maintenance and logistics — along with a deep local talent pool and plenty of career options.
Career Path: This role can lead toward Senior AI Engineer, AI Architect, ML Platform Lead, and Head of AI Engineering positions.
Who Should Apply?
- Generative AI Engineers: Who have shipped LLM-based applications into production, not only prototypes
- Python Developers: With strong software engineering foundations moving into agentic AI and RAG work
- Agentic Systems Builders: Experienced with LangChain, LangGraph, Semantic Kernel, or similar orchestration frameworks
- MLOps-Minded Engineers: Comfortable with monitoring, evaluation, cost control, and drift detection for deployed models
- DFW-Based Tech Professionals: Ready for a Fort Worth role, with airline or transport domain knowledge as a bonus
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